Contour Animation

# Built-in topographic data
filled.contour(volcano,
               color.palette = terrain.colors,
               main = "Topographic Contour Map Showing Elevation of Land",
               xlab = "East-West", ylab = "North-South")

library(ggplot2)

# Simulated rainfall data
set.seed(123)
lat <- runif(50, 25, 30)   # latitude range
lon <- runif(50, 75, 80)   # longitude range
rain <- rnorm(50, mean = 100, sd = 20)

df <- data.frame(lon, lat, rain)

# Interpolation grid
library(akima)
interp_data <- with(df, interp(lon, lat, rain, duplicate = "mean"))

interp_df <- data.frame(
  lon = rep(interp_data$x, times = length(interp_data$y)),
  lat = rep(interp_data$y, each = length(interp_data$x)),
  rain = as.vector(interp_data$z)
)

ggplot(interp_df, aes(lon, lat, z = rain)) +
  geom_contour_filled(bins = 10) +
  scale_fill_viridis_d(option = "C") +
  labs(title = "Rainfall Contour Map", x = "Longitude", y = "Latitude") +
  theme_minimal()

## Warning: Removed 321 rows containing non-finite outside the scale range
## (`stat_contour_filled()`).

# Simulated temperature data
set.seed(42)
lat <- runif(40, 20, 25)
lon <- runif(40, 75, 82)
temp <- 25 + 5 * sin(lat) + rnorm(40)

df <- data.frame(lon, lat, temp)

# Interpolation
interp_data <- with(df, interp(lon, lat, temp, duplicate = "mean"))

interp_df <- data.frame(
  lon = rep(interp_data$x, times = length(interp_data$y)),
  lat = rep(interp_data$y, each = length(interp_data$x)),
  temp = as.vector(interp_data$z)
)

ggplot(interp_df, aes(lon, lat, z = temp)) +
  geom_contour_filled(bins = 12) +
  scale_fill_viridis_d(option = "A") +
  labs(title = "Temperature Variation", x = "Longitude", y = "Latitude") +
  theme_minimal()

## Warning: Removed 315 rows containing non-finite outside the scale range
## (`stat_contour_filled()`).

set.seed(999)
lat <- runif(60, 28.4, 28.8)  # Delhi region
lon <- runif(60, 77.0, 77.6)
pm25 <- rnorm(60, mean = 150, sd = 30)

df <- data.frame(lon, lat, pm25)
interp_data <- with(df, interp(lon, lat, pm25, duplicate = "mean"))

interp_df <- data.frame(
  lon = rep(interp_data$x, times = length(interp_data$y)),
  lat = rep(interp_data$y, each = length(interp_data$x)),
  pm25 = as.vector(interp_data$z)
)

ggplot(interp_df, aes(lon, lat, z = pm25)) +
  geom_contour_filled(bins = 8) +
  scale_fill_viridis_d(option = "E") +
  labs(title = "PM2.5 Contour Map (Air Quality)", x = "Longitude", y = "Latitude") +
  theme_minimal()

## Warning: Removed 351 rows containing non-finite outside the scale range
## (`stat_contour_filled()`).

# 1. Load Libraries
# You may need to install GSODR: install.packages("GSODR")
library(leaflet)
library(raster)

## Loading required package: sp

library(sf)

## Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUE

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:raster':
## 
##     intersect, select, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(GSODR)

# 2. Fetch and Prepare Real Weather Data
# Fetch weather data for the US, Canada, and Mexico for 2024
weather_data <- get_GSOD(years = 2024, country = c("US", "CA", "MX"))

# Clean the data: filter to one day, keep relevant columns, remove NAs
# CORRECTED LINE: Using lowercase 'latitude', 'longitude', and 'temp'
temp_data_one_day <- weather_data %>%
  filter(MONTH == 6, DAY == 15) %>%
  select(LATITUDE, LONGITUDE, TEMP) %>%
  na.omit()

# Convert to an sf object, specifying the corrected column names
temp_sf <- st_as_sf(temp_data_one_day, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)

# 3. Interpolate and Generate Contours
# Create a raster grid covering the extent of the data points
grid <- raster(extent(temp_sf), resolution = 0.5)

# Interpolate the temperature readings onto the grid
temp_raster <- rasterize(temp_sf, grid, field = 'TEMP', fun = function(x, ...) mean(x, na.rm = TRUE))
interpolated_raster <- disaggregate(temp_raster, fact = 4) %>%
                       resample(grid, method = "bilinear")

# Generate contour lines and convert to sf
contour_lines <- rasterToContour(interpolated_raster, nlevels = 12)
contour_lines_sf <- st_as_sf(contour_lines)

# IMPORTANT: Convert the temperature 'level' to numeric
contour_lines_sf$level <- as.numeric(contour_lines_sf$level)

# 4. Build the Interactive Map
# Create a diverging color palette for temperature
pal <- colorNumeric(palette = "RdYlBu", domain = contour_lines_sf$level, reverse = TRUE)

# Create the map
temperature_map <- leaflet() %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  # Add the original weather station points
  addCircleMarkers(
    data = temp_sf,
    radius = 3,
    color = "black",
    stroke = FALSE,
    fillOpacity = 0.5
    #popup = ~paste("Temp:", round(temp, 1), "°C")
  ) %>%
  # Add the temperature contour polygons
  addPolygons(
    data = contour_lines_sf,
    fillColor = ~pal(level),
    color = "white",
    weight = 1,
    fillOpacity = 0.6,
    #label = ~paste("Temp:", round(level, 1), "°C")
  ) %>%
  # Add a legend
  addLegend(
    pal = pal,
    values = contour_lines_sf$level,
    title = "Temperature (°C)",
    position = "bottomright"
  )

# Display the map
temperature_map

# Load libraries
#library(ggplot2)
#library(gganimate)
#library(dplyr)

# Create synthetic data: x, y grid with elevation changing over time#
#set.seed(123)
#grid <- expand.grid(x = seq(-2, 2, length.out = 50),
 #                   y = seq(-2, 2, length.out = 50),
  #                  time = 1:20)

#grid$z <- with(grid, exp(-(x^2 + y^2)) * cos(time/3) * 100)

# Plot animated contour
#p <- ggplot(grid, aes(x, y, z = z)) +
 # geom_contour_filled() +
  #labs(title = "Animated Contour Map (Elevation over Time: {frame_time})",
   #    x = "Longitude", y = "Latitude") +
  #transition_time(time) +
  #ease_aes('linear')

#animate(p, nframes = 40, fps = 5, width = 600, height = 600, renderer = #gifski_renderer())

# Load libraries
library(ggplot2)
library(dplyr)

# 1. Create a synthetic "temperature surface"
set.seed(123)
grid <- expand.grid(
  lon = seq(-100, -80, length.out = 50),
  lat = seq(30, 45, length.out = 50)
)

# Define a smooth temperature field (not random noise only!)
grid$temp <- with(grid, 25 + 5*sin(lon/5) + 3*cos(lat/5))

# 2. Plot contour map
p <- ggplot(grid, aes(lon, lat, z = temp)) +
  geom_contour_filled(bins = 10) +
  labs(title = "Simulated Temperature Contours",
       x = "Longitude", y = "Latitude", fill = "Temp (°C)") +
  theme_minimal()

print(p)

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:raster':
## 
##     select

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# Use volcano dataset
z <- volcano

# Create a 3D surface plot with contours
plot_ly(z = ~z) %>%
  add_surface(contours = list(
    z = list(show=TRUE, usecolormap=TRUE, highlightcolor="limegreen", project=list(z=TRUE))
  )) %>%
  layout(title = "3D Volcano Surface with Contours")